These slides were prepared for the 2019 Vanderbilt Mouse Kidney Injury Workshop (link) on 13 August 2019.

These slides are available on my faculty page under Teaching

tgstewart.xyz

• provide a big picture overview of power calculations
• explain when power calculations are (and are not) appropriate
• introduce online resources for power calculations
• work through hands-on examples of calculating power

Operating characteristic:

The distribution of a study trait over repeated executions of the study.

1. Sample size
2. False positive rate
3. False negative rate
4. False direction rate

1. Sample size
2. False positive rate (Type I error)
3. False negative rate (Type II error)
4. False direction rate (Type S error)

Who cares about operating characteristics?

A researcher should understand the sample size needed for a study to generate conclusive results.

Operating characteristics are helpful quantities for grant reviewers and funding agencies and anyone who must judge the likelihood that a study will generate conclusive results within budget.

Data scientists calculate operating characteristics to understand the performance of a proposed analysis method.

When should we care about power?

Grant reviewers generally prefer to fund projects that will lead to conclusive results.

Data scientists prefer that study analysis plans use methods that maximize power.

When should we NOT care about power?

Grant reviewers generally prefer to fund projects that will lead to conclusive results.

Data scientists prefer that study analysis plans use methods that maximize power.